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Process monitoring framework for cross-flow diafiltration-based virus-like particle disassembly: Tracing product properties and filtration performance.

Authors
  • Hillebrandt, Nils1
  • Vormittag, Philipp1
  • Dietrich, Annabelle1
  • Hubbuch, Jürgen1
  • 1 Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany. , (Germany)
Type
Published Article
Journal
Biotechnology and Bioengineering
Publisher
Wiley (John Wiley & Sons)
Publication Date
Jun 01, 2022
Volume
119
Issue
6
Pages
1522–1538
Identifiers
DOI: 10.1002/bit.28063
PMID: 35170757
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Virus-like particles (VLPs) are an emerging biopharmaceutical modality with great potential as a platform technology. VLPs can be applied as gene therapy vectors and prophylactic or therapeutic vaccines. For non-enveloped VLPs, recombinant production of the protein subunits leads to intracellular self-assembly. The subsequent purification process includes VLP dis- and reassembly which aim at removing encapsulated impurities and improving particle properties. Filtration-based separation and processing has proven successful for VLPs but requires large product quantities and laborious experiments in early development stages. Both challenges can be tackled by implementation of process analytical technology (PAT) to efficiently obtain extensive process information. In this study, an existing PAT setup was extended to comprehensively monitor the diafiltration-based disassembly of hepatitis B core antigen (HBcAg) VLPs. Process-related signals were monitored in-line, while product-related signals, such as ultraviolet light (UV) spectra as well as static and dynamic light scattering (SLS and DLS), were monitored on-line. The applicability of the sensors for disassembly monitoring was evaluated under varying processing conditions. HBcAg VLP subunit concentrations were accurately predicted based on UV data using ordinary and partial least squares regression models (Q2 from 0.909 to 0.976). DLS data were used for aggregation monitoring while the SLS intensity qualitatively reflected the disassembly progress. © 2022 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.

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